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Distributed Resource Management for Wireless Networks Over Unlicensed Spectrum
Title
Distributed Resource Management for Wireless Networks Over Unlicensed Spectrum
Creator
Han, Mengqi
Date
2020
Description
In the past decades, a variety of wireless networks have been deployed, e.g., long term evolution (LTE) cellular networks, wireless local...
Show moreIn the past decades, a variety of wireless networks have been deployed, e.g., long term evolution (LTE) cellular networks, wireless local network networks (WLANs), cloud radio access network (C-RANs), wireless metropolitan area networks (WMANs), wireless body area networks (WBANs) and etc.To meet the exponential growth of traffic demands and improve the network throughput, different enhancement in the MAC protocols have been proposed for the emerging networks. For example, U-LTE (Unlicensed LTE) is proposed for LTE users to aggregate the spacious unlicensed spectrum with the licensed spectrum to boost the network throughput. Meanwhile, Wi-Fi users are allowed to opportunistically bond available channels for high data rate transmissions to improve the spectrum efficiency and network throughput. But the performance of the emerging networks with the new techniques has not been well investigated. Thus, in this thesis, we comprehensively investigate the network performance in different network scenarios. In each scenario, we first develop mathematical models to identify the performance bottlenecks in the existing MAC protocols. We then propose an algorithm to intelligently tune the protocol parameters to maximize network performance. Finally, the proposed algorithm is compared with some existing algorithms. Specifically, in the first scenario, we evaluate the coexistence performance between the Wi-Fi users with channel bonding capability and the legacy users without channel bonding capability. Specifically, the channel bonding probability and the channel access delay of wireless users are first analyzed, considering the contentions among legacy and multi-channel users in the same channel and across multiple channels. Based on the analysis, the network capacity, i.e., the maximum number of traffic flows that can be supported with the bounded delay performance in a multi-channel Wi-Fi with and without channel bonding, is then derived. Based on the analytical results, we propose a heuristic bonding policythat can provide important guidelines to control the number of flows to satisfy the QoS requirement and achieve the maximum network capacity. In addition, we propose an efficient probabilistic channel aggregation scheme to maximize the network throughput under the quality of service constraints for multi-channel users with channel aggregation capability. A Proximal Policy Optimization (PPO) based approach is further applied to intelligently tune the aggregating probabilities of secondary channels to maximize the network throughput.In the second scenario, we consider that U-LTE users are coexisting with the legacy users without channel bonding capability in the same unlicensed spectrum. The throughput of both Wi-Fi and U-LTE users are both derived when U-LTE users adopting two Load Based Equipment(LBE) random access protocols and Category 4 (Cat 4) algorithm agreed in 3GPP release 13.Based on the analysis, we find that the current protocols of U-LTE users are far from perfect to achieve harmony coexistence. Subject to the system fairness constraint, the aggregate throughput of U-LTE and Wi-Fi networks is maximized based on a semi branch and bound algorithm. To make the complex optimization tractable, reinforcement learning techniques are introduced to intelligently tune the contention window size for both U-LTE and Wi-Fi users. Specifically, a cooperative learning algorithm is developed assuming that the information between different systems is exchangeable. A non-cooperative version is subsequently developed to remove the previous assumption for better practicability. Extensive simulations are conducted to demonstrate the performance of the proposed learning algorithms in contrast to the analytical upper bound under various conditions. It is shown that both proposed learning algorithms can significantly improve the total throughput performance while satisfying the fairness constraints.Finally, by considering the energy constraints, we consider an IoT network where IoT devices use adaptive p-persistent ALOHA for data transmissions. In an IoT network with energy harvesting, an IoT device can contend for channel access only when it is ready, i.e., it has data for transmission and it harvests enough energy for communications. Due to stochastic energy harvesting and random access, the number of ready devices in the network may vary. As such, an analytical framework is first developed using a discrete Markov model to analyze the average number of ready devices. Next, an optimization problem is formulated to maximize the system throughput by adapting the transmission probability p of IoT devices. Given that the wireless environment is unknown at different IoT devices, e.g., the total number of contending devices, data arrival rates of other IoT devices, a multi-agent reinforcement learning algorithm is introduced for each device to autonomously tune the transmission probability in a distributed manner. In addition, game theory is applied to design the reward function to ensure an equilibrium and to closely approach the optimal parameter setting. Numerical results show that the proposed learning algorithm can greatly improve the throughput performance comparing with other algorithms.
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